Lossy data compression has numerous applications, especially in communications, broadcasting, entertainment, and security. Video compression is a challenging task, because large compression ratios are required to transmit high-quality and high-resolution pictures over existing communication channels. This task is even more challenging in the context of wireless and mobile communications, or real-time encoding of media.
The recently adopted ITU-T H.265/HEVC standard (ISO/IEC 23008-2:2013, “Information technology—High efficiency coding and media delivery in heterogeneous environments—Part 2: High efficiency video coding”, November 2013) declares a set of state-of-the-art video coding tools that provide a reasonable tradeoff between coding efficiency and computational complexity. An overview on the ITU-T H.265/HEVC standard is given in the article by Gary J. Sullivan, “Overview of the High Efficiency Video Coding (HEVC) Standard”, in IEEE Transactions on Circuits and Systems for Video Technology, Vol. 22, No. 12, December 2012, the entire content of which is incorporated herein by reference.
Similarly to the ITU-T H.264/AVC video coding standard, the HEVC/H.265 video coding standard provides for a division of the source picture into blocks, e.g. coding units (CUs). Each of the CUs could be further split into either smaller CUs or prediction units (PUs). A PU could be intra- or inter-predicted according to the type of processing applied for the pixels of PU. In case of inter-prediction, a PU represents an area of pixels that is processed by motion compensation using a motion vector specified for a PU. For intra prediction PU specifies prediction mode for a set of transform units (TUs). A TU can have different sizes (e.g., 4×4, 8×8, 16×16 and 32×32 pixels) and could be processed in different ways. For a TU transform coding is being performed, i.e. the prediction error is being transformed with a discrete cosine transform (DCT) and quantized. Resulting quantized transform coefficients are grouped into CGs, each CG having 16 quantized transform coefficients.
As noted above, the core tools of these standards or similar proprietary codecs to encode the blocks of picture are inter- and intra-prediction, spectrum-transformation (e.g., discrete cosine transform or its integer approximation) and quantization. Inter- and intra-prediction tools are used to generate a prediction signal for a given block. At the encoder side, the difference between a source block and its prediction, the so-called residual signal, is transformed into their spectrum, i.e. source block pixels are represented by transform coefficients in frequency domain. Further, the coefficients are quantized. Non-zero and zero quantized transform coefficients are often referred to as significant and insignificant coefficients, respectively. All syntax elements including quantized transform coefficients and side information (e.g., intra prediction modes for intra-coding and motion vectors for inter-coding) are binarized and entropy encoded. The part of entropy encoded coefficients in a compressed H.265/HEVC bit-stream may exceed 80%.
The stages of encoding quantized transform coefficients are as follows:                Encoding the position of last significant coefficient, i.e. the last non-zero quantized transform coefficient.        Encoding the significance map that is used to restore positions of all the non-zero coefficients.        Sign encoding of the significant coefficients.        Magnitude encoding of the significant coefficients.        
These stages are performed in the context of quantized transform coefficients being split into so-called coefficient groups (CGs). Each CG is a subset that typically consists of 4×4 coefficients.
Explicit sign encoding requires one sign bit per one significant coefficient to be encoded. However, a new tool referred to as Sign Bit Hiding (SBH) has been adopted for the ITU-T H.265/HEVC standard. The basic idea behind this technique is to implicitly indicate the sign of the first significant coefficient within a given CG using a parity check of the sum of least significant bits of the significant coefficients belonging to that CG. This tool is applied not to all the CGs but just to those CGs that meet the threshold condition, i.e. the difference of positions of the first and last significant coefficients should be more or equal to four. According to the results presented by the Joint Collaborative Team on Video Coding (JCT-VC) that was responsible for developing the H.265/HEVC standard, this tool reduces the bit-rate for the same quality for a wide range of video sequences used in JCT-VC tests. This confirms that SBH, in particular, and data hiding, in general, can be an efficient compression tool.
In addition to video compression, data hiding can be used in different Digital Rights Management (DRM) applications (e.g., digital watermarking). It is one of the DRM technologies used to embed digital information in a carrier signal (see for example Tirkel et. al., “Electronic Water Mark”, Digital Image Computing: Techniques and Applications (DICTA), 1993, Macquarie University, pp. 666-673). This hidden information could be used to verify the authenticity or integrity of the carrier signal or to show the identity of its owners.
In the case, if several hiding operations should be performed on the same set of target values these operations can potentially interfere with each other. This interfering occurs if hiding operation modifies a value of the set without taking into account the effect of this modification on the data hidden during previous hiding operations. So, a simple combination of hiding operations can result in producing an undecodable bit-stream.
As an example of such a situation we could consider hiding of a set of flags within quantized transform coefficients of a TU when SBH should be performed for these coefficients as well. If the flag hiding operation is performed independently (i.e. it is not matched with SBH), extracting hidden signs can be performed incorrectly.